Overview

Dataset statistics

Number of variables14
Number of observations38765
Missing cells164
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory112.0 B

Variable types

Numeric13
DateTime1

Alerts

df_index is highly correlated with sensorParms(5)High correlation
_RS232Dpt is highly correlated with sensorParms(1) and 1 other fieldsHigh correlation
sensorParms(1) is highly correlated with _RS232Dpt and 2 other fieldsHigh correlation
sensorParms(2) is highly correlated with sensorParms(1)High correlation
sensorParms(5) is highly correlated with df_indexHigh correlation
sensorParms(7) is highly correlated with sensorParms(8)High correlation
sensorParms(8) is highly correlated with sensorParms(7)High correlation
sensorParms(9) is highly correlated with _RS232Dpt and 1 other fieldsHigh correlation
sensorParms(10) is highly correlated with sensorParms(11)High correlation
sensorParms(11) is highly correlated with sensorParms(10)High correlation
df_index is highly correlated with sensorParms(5) and 1 other fieldsHigh correlation
_RS232Dpt is highly correlated with sensorParms(1) and 1 other fieldsHigh correlation
sensorParms(1) is highly correlated with _RS232Dpt and 3 other fieldsHigh correlation
sensorParms(2) is highly correlated with sensorParms(1) and 1 other fieldsHigh correlation
sensorParms(5) is highly correlated with df_index and 1 other fieldsHigh correlation
sensorParms(6) is highly correlated with df_index and 1 other fieldsHigh correlation
sensorParms(7) is highly correlated with sensorParms(8)High correlation
sensorParms(8) is highly correlated with sensorParms(7)High correlation
sensorParms(9) is highly correlated with _RS232Dpt and 1 other fieldsHigh correlation
sensorParms(10) is highly correlated with sensorParms(11)High correlation
sensorParms(11) is highly correlated with sensorParms(1) and 2 other fieldsHigh correlation
_RS232Dpt is highly correlated with sensorParms(9)High correlation
sensorParms(1) is highly correlated with sensorParms(2)High correlation
sensorParms(2) is highly correlated with sensorParms(1)High correlation
sensorParms(7) is highly correlated with sensorParms(8)High correlation
sensorParms(8) is highly correlated with sensorParms(7)High correlation
sensorParms(9) is highly correlated with _RS232DptHigh correlation
sensorParms(10) is highly correlated with sensorParms(11)High correlation
sensorParms(11) is highly correlated with sensorParms(10)High correlation
df_index is highly correlated with sensorParms(2) and 3 other fieldsHigh correlation
_RS232Dpt is highly correlated with sensorParms(1) and 2 other fieldsHigh correlation
sensorParms(1) is highly correlated with _RS232Dpt and 9 other fieldsHigh correlation
sensorParms(2) is highly correlated with df_index and 11 other fieldsHigh correlation
sensorParms(3) is highly correlated with df_index and 9 other fieldsHigh correlation
sensorParms(4) is highly correlated with sensorParms(1) and 6 other fieldsHigh correlation
sensorParms(5) is highly correlated with sensorParms(1) and 6 other fieldsHigh correlation
sensorParms(6) is highly correlated with df_index and 9 other fieldsHigh correlation
sensorParms(7) is highly correlated with sensorParms(2) and 5 other fieldsHigh correlation
sensorParms(8) is highly correlated with sensorParms(1) and 8 other fieldsHigh correlation
sensorParms(9) is highly correlated with _RS232Dpt and 2 other fieldsHigh correlation
sensorParms(10) is highly correlated with sensorParms(1) and 8 other fieldsHigh correlation
sensorParms(11) is highly correlated with df_index and 7 other fieldsHigh correlation
sensorParms(4) is highly skewed (γ1 = 36.49924599) Skewed
sensorParms(8) is highly skewed (γ1 = 20.25822299) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
TIMESTAMP has unique values Unique
sensorParms(5) has 15194 (39.2%) zeros Zeros

Reproduction

Analysis started2022-08-30 20:13:52.567306
Analysis finished2022-08-30 20:14:50.876529
Duration58.31 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct38765
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19384
Minimum2
Maximum38766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.0 KiB
2022-08-30T22:14:51.054127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1940.2
Q19693
median19384
Q329075
95-th percentile36827.8
Maximum38766
Range38764
Interquartile range (IQR)19382

Descriptive statistics

Standard deviation11190.63593
Coefficient of variation (CV)0.577313038
Kurtosis-1.2
Mean19384
Median Absolute Deviation (MAD)9691
Skewness0
Sum751420760
Variance125230332.5
MonotonicityStrictly increasing
2022-08-30T22:14:51.305103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21
 
< 0.1%
258401
 
< 0.1%
258421
 
< 0.1%
258431
 
< 0.1%
258441
 
< 0.1%
258451
 
< 0.1%
258461
 
< 0.1%
258471
 
< 0.1%
258481
 
< 0.1%
258491
 
< 0.1%
Other values (38755)38755
> 99.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
387661
< 0.1%
387651
< 0.1%
387641
< 0.1%
387631
< 0.1%
387621
< 0.1%
387611
< 0.1%
387601
< 0.1%
387591
< 0.1%
387581
< 0.1%
387571
< 0.1%

TIMESTAMP
Date

UNIQUE

Distinct38765
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size303.0 KiB
Minimum2020-06-09 15:14:06
Maximum2022-08-19 01:18:29
2022-08-30T22:14:51.551564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:51.773082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

_RS232Dpt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3035
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.86470989
Minimum0.836
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.0 KiB
2022-08-30T22:14:52.111498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.836
5-th percentile4.045
Q116.09
median32.02
Q347.99
95-th percentile67.9
Maximum81
Range80.164
Interquartile range (IQR)31.9

Descriptive statistics

Standard deviation19.80243126
Coefficient of variation (CV)0.6025439241
Kurtosis-0.9299224244
Mean32.86470989
Median Absolute Deviation (MAD)15.93
Skewness0.2488451968
Sum1274000.479
Variance392.1362839
MonotonicityNot monotonic
2022-08-30T22:14:52.520317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.0686
 
0.2%
47.0683
 
0.2%
50.0782
 
0.2%
47.0882
 
0.2%
47.0782
 
0.2%
38.0780
 
0.2%
48.0880
 
0.2%
21.0980
 
0.2%
26.0780
 
0.2%
14.0879
 
0.2%
Other values (3025)37951
97.9%
ValueCountFrequency (%)
0.8361
< 0.1%
0.8391
< 0.1%
0.8441
< 0.1%
0.8481
< 0.1%
0.8491
< 0.1%
0.851
< 0.1%
0.8511
< 0.1%
0.8522
< 0.1%
0.8541
< 0.1%
0.8552
< 0.1%
ValueCountFrequency (%)
816
< 0.1%
80.91
 
< 0.1%
80.81
 
< 0.1%
80.71
 
< 0.1%
80.52
 
< 0.1%
80.22
 
< 0.1%
80.12
 
< 0.1%
8011
< 0.1%
79.991
 
< 0.1%
79.981
 
< 0.1%

sensorParms(1)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8632
Distinct (%)22.3%
Missing135
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean6.186702874
Minimum2.815
Maximum36.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.0 KiB
2022-08-30T22:14:52.751567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.815
5-th percentile3.655
Q14.795
median5.376
Q36.461
95-th percentile12.73055
Maximum36.56
Range33.745
Interquartile range (IQR)1.666

Descriptive statistics

Standard deviation2.707698267
Coefficient of variation (CV)0.4376641844
Kurtosis9.431374419
Mean6.186702874
Median Absolute Deviation (MAD)0.7
Skewness2.593844343
Sum238992.332
Variance7.331629908
MonotonicityNot monotonic
2022-08-30T22:14:52.957209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.80535
 
0.1%
4.68134
 
0.1%
4.83733
 
0.1%
4.81632
 
0.1%
5.24432
 
0.1%
4.78131
 
0.1%
4.84231
 
0.1%
4.78230
 
0.1%
4.77830
 
0.1%
4.77430
 
0.1%
Other values (8622)38312
98.8%
(Missing)135
 
0.3%
ValueCountFrequency (%)
2.8151
 
< 0.1%
2.8164
< 0.1%
2.8172
 
< 0.1%
2.8181
 
< 0.1%
2.8195
< 0.1%
2.823
< 0.1%
2.8212
 
< 0.1%
2.8222
 
< 0.1%
2.8234
< 0.1%
2.8241
 
< 0.1%
ValueCountFrequency (%)
36.561
< 0.1%
34.731
< 0.1%
34.491
< 0.1%
32.711
< 0.1%
31.071
< 0.1%
30.731
< 0.1%
30.651
< 0.1%
30.531
< 0.1%
30.451
< 0.1%
30.431
< 0.1%

sensorParms(2)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1468
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.21090881
Minimum26.66
Maximum60.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.0 KiB
2022-08-30T22:14:53.184293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum26.66
5-th percentile27.42
Q129.06
median29.68
Q330.62
95-th percentile35.84
Maximum60.21
Range33.55
Interquartile range (IQR)1.56

Descriptive statistics

Standard deviation2.384660191
Coefficient of variation (CV)0.07893374563
Kurtosis7.36362619
Mean30.21090881
Median Absolute Deviation (MAD)0.73
Skewness2.24755284
Sum1171125.88
Variance5.686604228
MonotonicityNot monotonic
2022-08-30T22:14:53.403017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.1264
 
0.7%
29.14239
 
0.6%
29.21238
 
0.6%
29.11230
 
0.6%
29.18229
 
0.6%
29.08228
 
0.6%
29.2227
 
0.6%
29.13224
 
0.6%
29.06222
 
0.6%
29.22221
 
0.6%
Other values (1458)36443
94.0%
ValueCountFrequency (%)
26.662
 
< 0.1%
26.679
 
< 0.1%
26.685
 
< 0.1%
26.6916
 
< 0.1%
26.727
0.1%
26.7129
0.1%
26.7225
0.1%
26.7335
0.1%
26.7424
0.1%
26.7540
0.1%
ValueCountFrequency (%)
60.211
< 0.1%
49.421
< 0.1%
492
< 0.1%
48.831
< 0.1%
47.721
< 0.1%
47.691
< 0.1%
47.661
< 0.1%
47.651
< 0.1%
47.641
< 0.1%
47.631
< 0.1%

sensorParms(3)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct566
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.14262582
Minimum0.02
Maximum96.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.0 KiB
2022-08-30T22:14:53.632763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile44.72
Q146.22
median47.35
Q347.75
95-th percentile49.01
Maximum96.36
Range96.34
Interquartile range (IQR)1.53

Descriptive statistics

Standard deviation1.759320023
Coefficient of variation (CV)0.03731909271
Kurtosis398.5145838
Mean47.14262582
Median Absolute Deviation (MAD)0.59
Skewness-13.90873338
Sum1827483.89
Variance3.095206945
MonotonicityNot monotonic
2022-08-30T22:14:53.845926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.32431
 
1.1%
47.33418
 
1.1%
47.65393
 
1.0%
47.31388
 
1.0%
47.34382
 
1.0%
47.63374
 
1.0%
47.64370
 
1.0%
47.3359
 
0.9%
46.2339
 
0.9%
47.66337
 
0.9%
Other values (556)34974
90.2%
ValueCountFrequency (%)
0.0229
0.1%
43.861
 
< 0.1%
43.882
 
< 0.1%
43.893
 
< 0.1%
43.98
 
< 0.1%
43.9113
< 0.1%
43.9226
0.1%
43.9320
0.1%
43.9417
< 0.1%
43.958
 
< 0.1%
ValueCountFrequency (%)
96.361
 
< 0.1%
49.52
 
< 0.1%
49.491
 
< 0.1%
49.485
 
< 0.1%
49.476
 
< 0.1%
49.4614
 
< 0.1%
49.4533
0.1%
49.4439
0.1%
49.4332
0.1%
49.4246
0.1%

sensorParms(4)
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02181478137
Minimum-4.35
Maximum12.08
Zeros3
Zeros (%)< 0.1%
Negative10
Negative (%)< 0.1%
Memory size303.0 KiB
2022-08-30T22:14:54.035131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.35
5-th percentile0.02
Q10.02
median0.02
Q30.02
95-th percentile0.02
Maximum12.08
Range16.43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1645816445
Coefficient of variation (CV)7.544501209
Kurtosis2398.917987
Mean0.02181478137
Median Absolute Deviation (MAD)0
Skewness36.49924599
Sum845.65
Variance0.02708711769
MonotonicityNot monotonic
2022-08-30T22:14:54.214951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0238735
99.9%
-4.344
 
< 0.1%
03
 
< 0.1%
-4.353
 
< 0.1%
6.332
 
< 0.1%
6.522
 
< 0.1%
6.462
 
< 0.1%
11.911
 
< 0.1%
12.081
 
< 0.1%
0.041
 
< 0.1%
Other values (11)11
 
< 0.1%
ValueCountFrequency (%)
-4.353
 
< 0.1%
-4.344
 
< 0.1%
-4.331
 
< 0.1%
-4.31
 
< 0.1%
-4.081
 
< 0.1%
03
 
< 0.1%
0.0238735
99.9%
0.041
 
< 0.1%
6.31
 
< 0.1%
6.311
 
< 0.1%
ValueCountFrequency (%)
12.081
< 0.1%
11.911
< 0.1%
6.531
< 0.1%
6.522
< 0.1%
6.511
< 0.1%
6.51
< 0.1%
6.481
< 0.1%
6.462
< 0.1%
6.451
< 0.1%
6.341
< 0.1%

sensorParms(5)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct722
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.601192829
Minimum-4.35
Maximum141.87
Zeros15194
Zeros (%)39.2%
Negative5656
Negative (%)14.6%
Memory size303.0 KiB
2022-08-30T22:14:54.426352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.35
5-th percentile-4.34
Q10
median0
Q36.48
95-th percentile6.67
Maximum141.87
Range146.22
Interquartile range (IQR)6.48

Descriptive statistics

Standard deviation5.3012326
Coefficient of variation (CV)2.038000621
Kurtosis171.4749783
Mean2.601192829
Median Absolute Deviation (MAD)4.34
Skewness7.626303531
Sum100835.24
Variance28.10306708
MonotonicityNot monotonic
2022-08-30T22:14:54.642358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015194
39.2%
-4.351269
 
3.3%
6.671260
 
3.3%
-4.341187
 
3.1%
6.53870
 
2.2%
6.54854
 
2.2%
6.52783
 
2.0%
6.5752
 
1.9%
6.51720
 
1.9%
-4.33716
 
1.8%
Other values (712)15160
39.1%
ValueCountFrequency (%)
-4.351269
3.3%
-4.341187
3.1%
-4.33716
1.8%
-4.32580
1.5%
-4.31368
 
0.9%
-4.3200
 
0.5%
-4.29147
 
0.4%
-4.2887
 
0.2%
-4.2752
 
0.1%
-4.2654
 
0.1%
ValueCountFrequency (%)
141.871
< 0.1%
138.581
< 0.1%
137.331
< 0.1%
137.31
< 0.1%
137.181
< 0.1%
137.091
< 0.1%
136.31
< 0.1%
136.131
< 0.1%
135.621
< 0.1%
135.61
< 0.1%

sensorParms(6)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct3195
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.9819285
Minimum-1.39
Maximum155.73
Zeros1
Zeros (%)< 0.1%
Negative7
Negative (%)< 0.1%
Memory size303.0 KiB
2022-08-30T22:14:54.871809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.39
5-th percentile96.4
Q199.67
median102.81
Q3133.09
95-th percentile138.25
Maximum155.73
Range157.12
Interquartile range (IQR)33.42

Descriptive statistics

Standard deviation16.65983778
Coefficient of variation (CV)0.1501130679
Kurtosis0.3925886157
Mean110.9819285
Median Absolute Deviation (MAD)4.11
Skewness0.6430477121
Sum4302214.46
Variance277.5501947
MonotonicityNot monotonic
2022-08-30T22:14:55.089848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.5174
 
0.2%
101.7266
 
0.2%
96.5564
 
0.2%
103.6563
 
0.2%
103.5463
 
0.2%
101.3661
 
0.2%
96.5460
 
0.2%
101.659
 
0.2%
103.4857
 
0.1%
103.6157
 
0.1%
Other values (3185)38141
98.4%
ValueCountFrequency (%)
-1.391
< 0.1%
-1.351
< 0.1%
-0.731
< 0.1%
-0.691
< 0.1%
-0.571
< 0.1%
-0.111
< 0.1%
-0.021
< 0.1%
01
< 0.1%
0.061
< 0.1%
0.11
< 0.1%
ValueCountFrequency (%)
155.731
< 0.1%
155.261
< 0.1%
155.091
< 0.1%
154.991
< 0.1%
154.841
< 0.1%
154.641
< 0.1%
154.61
< 0.1%
154.311
< 0.1%
154.291
< 0.1%
154.151
< 0.1%

sensorParms(7)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct403
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2549722688
Minimum-2.24
Maximum36.92
Zeros194
Zeros (%)0.5%
Negative21126
Negative (%)54.5%
Memory size303.0 KiB
2022-08-30T22:14:55.314198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.24
5-th percentile-1.94
Q1-0.89
median-0.22
Q30.2
95-th percentile1.33
Maximum36.92
Range39.16
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation0.9763707822
Coefficient of variation (CV)-3.829321466
Kurtosis53.41205635
Mean-0.2549722688
Median Absolute Deviation (MAD)0.5
Skewness1.428705932
Sum-9884
Variance0.9532999044
MonotonicityNot monotonic
2022-08-30T22:14:55.528163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.131811
 
4.7%
0.121243
 
3.2%
0.11989
 
2.6%
0.14495
 
1.3%
0.1405
 
1.0%
0.15353
 
0.9%
1.35338
 
0.9%
-0.98318
 
0.8%
1.34303
 
0.8%
-0.62299
 
0.8%
Other values (393)32211
83.1%
ValueCountFrequency (%)
-2.241
 
< 0.1%
-2.234
 
< 0.1%
-2.223
 
< 0.1%
-2.211
 
< 0.1%
-2.21
 
< 0.1%
-2.191
 
< 0.1%
-2.186
< 0.1%
-2.175
 
< 0.1%
-2.1610
< 0.1%
-2.1513
< 0.1%
ValueCountFrequency (%)
36.921
 
< 0.1%
6.091
 
< 0.1%
2.531
 
< 0.1%
2.061
 
< 0.1%
1.982
 
< 0.1%
1.978
< 0.1%
1.967
 
< 0.1%
1.9512
< 0.1%
1.9418
< 0.1%
1.9315
< 0.1%

sensorParms(8)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct432
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2273328002
Minimum-2.24
Maximum66.027
Zeros193
Zeros (%)0.5%
Negative21119
Negative (%)54.5%
Memory size303.0 KiB
2022-08-30T22:14:55.926491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.24
5-th percentile-1.94
Q1-0.89
median-0.22
Q30.2
95-th percentile1.33
Maximum66.027
Range68.267
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation1.504213734
Coefficient of variation (CV)-6.616791475
Kurtosis731.6880945
Mean-0.2273328002
Median Absolute Deviation (MAD)0.5
Skewness20.25822299
Sum-8812.556
Variance2.262658958
MonotonicityNot monotonic
2022-08-30T22:14:56.146899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.131810
 
4.7%
0.121240
 
3.2%
0.11988
 
2.5%
0.14494
 
1.3%
0.1404
 
1.0%
0.15352
 
0.9%
1.35338
 
0.9%
-0.98318
 
0.8%
1.34303
 
0.8%
-0.62299
 
0.8%
Other values (422)32219
83.1%
ValueCountFrequency (%)
-2.241
 
< 0.1%
-2.234
 
< 0.1%
-2.223
 
< 0.1%
-2.211
 
< 0.1%
-2.21
 
< 0.1%
-2.191
 
< 0.1%
-2.186
< 0.1%
-2.175
 
< 0.1%
-2.1610
< 0.1%
-2.1513
< 0.1%
ValueCountFrequency (%)
66.0271
< 0.1%
66.0021
< 0.1%
65.0621
< 0.1%
61.0951
< 0.1%
56.0531
< 0.1%
56.0411
< 0.1%
54.1131
< 0.1%
50.0571
< 0.1%
49.0441
< 0.1%
48.9811
< 0.1%

sensorParms(9)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11350
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.86431771
Minimum0.836
Maximum81.036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.0 KiB
2022-08-30T22:14:56.372525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.836
5-th percentile4.045
Q116.087
median32.023
Q347.987
95-th percentile67.902
Maximum81.036
Range80.2
Interquartile range (IQR)31.9

Descriptive statistics

Standard deviation19.80233482
Coefficient of variation (CV)0.6025481798
Kurtosis-0.9299153467
Mean32.86431771
Median Absolute Deviation (MAD)15.937
Skewness0.2488560722
Sum1273985.276
Variance392.1324642
MonotonicityNot monotonic
2022-08-30T22:14:56.604373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.08918
 
< 0.1%
47.0617
 
< 0.1%
9.12916
 
< 0.1%
12.06715
 
< 0.1%
50.04915
 
< 0.1%
44.06615
 
< 0.1%
45.06215
 
< 0.1%
37.09515
 
< 0.1%
39.06615
 
< 0.1%
21.09514
 
< 0.1%
Other values (11340)38610
99.6%
ValueCountFrequency (%)
0.8361
< 0.1%
0.8391
< 0.1%
0.8441
< 0.1%
0.8481
< 0.1%
0.8491
< 0.1%
0.851
< 0.1%
0.8511
< 0.1%
0.8522
< 0.1%
0.8541
< 0.1%
0.8552
< 0.1%
ValueCountFrequency (%)
81.0361
< 0.1%
81.0081
< 0.1%
80.9951
< 0.1%
80.9941
< 0.1%
80.9831
< 0.1%
80.9591
< 0.1%
80.8971
< 0.1%
80.8111
< 0.1%
80.6511
< 0.1%
80.531
< 0.1%

sensorParms(10)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct523
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.243073133
Minimum-0.08
Maximum25.85
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size303.0 KiB
2022-08-30T22:14:56.836005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.08
5-th percentile7.02
Q17.75
median8.13
Q38.59
95-th percentile9.71
Maximum25.85
Range25.93
Interquartile range (IQR)0.84

Descriptive statistics

Standard deviation0.9404754279
Coefficient of variation (CV)0.1140928162
Kurtosis54.83625849
Mean8.243073133
Median Absolute Deviation (MAD)0.4
Skewness3.244775247
Sum319542.73
Variance0.8844940305
MonotonicityNot monotonic
2022-08-30T22:14:57.061594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.77751
 
1.9%
7.76738
 
1.9%
7.78626
 
1.6%
7.75595
 
1.5%
7.74549
 
1.4%
7.79402
 
1.0%
8.25375
 
1.0%
8.24374
 
1.0%
7.73372
 
1.0%
8.26358
 
0.9%
Other values (513)33625
86.7%
ValueCountFrequency (%)
-0.081
 
< 0.1%
4.661
 
< 0.1%
4.681
 
< 0.1%
4.831
 
< 0.1%
4.853
< 0.1%
4.882
< 0.1%
4.893
< 0.1%
4.931
 
< 0.1%
4.951
 
< 0.1%
4.961
 
< 0.1%
ValueCountFrequency (%)
25.851
< 0.1%
25.821
< 0.1%
25.691
< 0.1%
25.631
< 0.1%
25.521
< 0.1%
25.511
< 0.1%
25.481
< 0.1%
25.471
< 0.1%
25.131
< 0.1%
24.981
< 0.1%

sensorParms(11)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1363
Distinct (%)3.5%
Missing29
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean23.41034851
Minimum-1.69
Maximum29.22
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size303.0 KiB
2022-08-30T22:14:57.292953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.69
5-th percentile19.52
Q122.14
median22.85
Q325
95-th percentile28.29
Maximum29.22
Range30.91
Interquartile range (IQR)2.86

Descriptive statistics

Standard deviation2.416969123
Coefficient of variation (CV)0.1032436199
Kurtosis1.521775682
Mean23.41034851
Median Absolute Deviation (MAD)1.16
Skewness-0.1383200357
Sum906823.26
Variance5.841739742
MonotonicityNot monotonic
2022-08-30T22:14:57.515018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.16310
 
0.8%
22.17295
 
0.8%
22.19274
 
0.7%
22.18262
 
0.7%
22.2255
 
0.7%
22.14246
 
0.6%
22.21245
 
0.6%
22.13244
 
0.6%
22.15238
 
0.6%
22.12224
 
0.6%
Other values (1353)36143
93.2%
ValueCountFrequency (%)
-1.691
< 0.1%
12.641
< 0.1%
12.821
< 0.1%
131
< 0.1%
13.111
< 0.1%
13.161
< 0.1%
13.351
< 0.1%
13.371
< 0.1%
13.41
< 0.1%
13.432
< 0.1%
ValueCountFrequency (%)
29.221
 
< 0.1%
29.091
 
< 0.1%
29.051
 
< 0.1%
29.041
 
< 0.1%
29.023
 
< 0.1%
29.015
< 0.1%
296
< 0.1%
28.998
< 0.1%
28.988
< 0.1%
28.9712
< 0.1%

Interactions

2022-08-30T22:14:46.189403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:06.291615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:09.739896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:13.206379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:16.600046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:20.288928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:23.377971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:26.686748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:29.927161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:33.212241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:36.478869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:39.801219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:43.170482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:46.443609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:06.587490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:10.003583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:13.449770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:16.877293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:20.559929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:23.659757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:26.955720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:30.240215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:33.499181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:36.945139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:40.139571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:43.437266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:46.686018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:06.867841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:10.256971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:13.700255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:17.141918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:20.795993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:23.893750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:27.217744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:30.682233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:33.727709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:37.178300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:40.377207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:43.686481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:46.946522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:07.095566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:10.507831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:13.975565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:17.409660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:21.043513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:24.142351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:27.469152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:30.895526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:33.978895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:37.405086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:40.612308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:43.908556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:47.178398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:07.350622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:10.743824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:14.220557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:17.665402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:21.293659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:24.399155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:27.705649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:31.130962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:34.216464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:37.660065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:40.868513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:44.145252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:47.567340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:07.576343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:10.973471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:14.450496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:17.899258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:21.538276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:24.781501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:27.951044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:31.347525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:34.443901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:37.871758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:41.086171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:44.391035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:47.795068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:07.844586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:11.237847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:14.685207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:18.144610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:21.787450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:25.021114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:28.171471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:31.585294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:34.677961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:38.102251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:41.317761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:44.631896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:48.010251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:08.165781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:11.491558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:14.950700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:18.386220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:21.995248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:25.261600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:28.420584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:31.806647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:34.926675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:38.321393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:41.548097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:44.854609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:48.237221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-30T22:14:08.455663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-30T22:14:58.067728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-30T22:14:58.391837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-30T22:14:58.707222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2022-08-30T22:14:49.976877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-30T22:14:50.463717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-30T22:14:50.616728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexTIMESTAMP_RS232DptsensorParms(1)sensorParms(2)sensorParms(3)sensorParms(4)sensorParms(5)sensorParms(6)sensorParms(7)sensorParms(8)sensorParms(9)sensorParms(10)sensorParms(11)
022020-06-09 15:14:060.91211.42834.7346.880.026.48142.911.051.050.9127.3119.23
132020-06-09 15:15:392.13410.80634.1846.890.026.48141.161.091.092.1347.6520.12
242020-06-09 15:17:113.14010.64934.0446.900.026.47141.021.081.083.1407.6920.24
352020-06-09 15:18:434.12210.37533.7946.890.026.45141.101.061.064.1227.7920.50
462020-06-09 15:20:155.10710.04333.5146.910.026.44140.441.061.065.1077.9020.78
572020-06-09 15:21:476.1328.99732.5946.930.026.43140.761.121.126.1328.2021.58
682020-06-09 15:23:197.1168.95332.5746.960.026.41140.731.091.097.1168.2321.67
792020-06-09 15:24:518.1208.71032.3646.970.026.40140.761.121.128.1158.3021.85
8102020-06-09 15:26:239.1107.99831.7246.970.026.39140.071.101.109.1068.5422.49
9112020-06-09 15:27:5610.1607.80331.5847.030.026.37140.021.111.1110.1638.6322.73

Last rows

df_indexTIMESTAMP_RS232DptsensorParms(1)sensorParms(2)sensorParms(3)sensorParms(4)sensorParms(5)sensorParms(6)sensorParms(7)sensorParms(8)sensorParms(9)sensorParms(10)sensorParms(11)
38755387572022-08-19 01:04:1541.045.89930.3447.770.026.32100.851.151.1541.0438.6723.11
38756387582022-08-19 01:05:4842.035.84230.2947.770.026.31100.651.171.1742.0308.6823.14
38757387592022-08-19 01:07:2443.085.77530.2547.800.026.31100.421.171.1743.0848.6823.15
38758387602022-08-19 01:08:5744.015.70530.1947.800.026.30100.471.181.1844.0128.7023.20
38759387612022-08-19 01:10:3045.025.69230.1847.820.026.31100.431.141.1445.0238.7023.21
38760387622022-08-19 01:12:0746.095.66630.1647.820.026.30100.431.161.1646.0898.7223.26
38761387632022-08-19 01:13:4347.115.64130.1547.830.026.30100.391.111.1147.1088.6923.16
38762387642022-08-19 01:15:1648.035.63230.1447.840.026.30100.291.161.1648.0328.7123.22
38763387652022-08-19 01:16:5349.125.62230.1447.840.026.30100.211.181.1849.1218.7323.27
38764387662022-08-19 01:18:2950.085.59930.1147.840.026.29100.031.161.1650.0768.7223.24